update

haoneng.lhn

2023-05-18 025df72c1002c0891d8ce6e37346724f69bfbdae

 funasr/bin/asr_infer.py

@@ -488,15 +488,20 @@

                nbest_hyps = nbest_hyps[: self.nbest]
            else:
                yseq = am_scores.argmax(dim=-1)
                score = am_scores.max(dim=-1)[0]
                score = torch.sum(score, dim=-1)
                # pad with mask tokens to ensure compatibility with sos/eos tokens
                yseq = torch.tensor(
                    [self.asr_model.sos] + yseq.tolist() + [self.asr_model.eos], device=yseq.device
                )
                if pre_token_length[i] == 0:
                    yseq = torch.tensor(
                        [self.asr_model.sos] + [self.asr_model.eos], device=yseq.device
                    )
                    score = torch.tensor(0.0, device=yseq.device)
                else:
                    yseq = am_scores.argmax(dim=-1)
                    score = am_scores.max(dim=-1)[0]
                    score = torch.sum(score, dim=-1)
                    # pad with mask tokens to ensure compatibility with sos/eos tokens
                    yseq = torch.tensor(
                        [self.asr_model.sos] + yseq.tolist() + [self.asr_model.eos], device=yseq.device
                    )
                nbest_hyps = [Hypothesis(yseq=yseq, score=score)]

            for hyp in nbest_hyps:
                assert isinstance(hyp, (Hypothesis)), type(hyp)